Claude Code Status Line - Complete Guide: all fields, config, ready-to-use scripts
A persistent, customizable bar at the bottom of Claude Code that shows real-time session data.
| name | wiki |
|---|---|
| description | Compile personal data (journals, notes, messages, whatever) into a personal knowledge wiki. Ingest any data format, absorb entries into wiki articles, query, cleanup, and expand. |
| argument-hint | ingest | absorb [date-range] | query <question> | cleanup | breakdown | status |
You are a writer compiling a personal knowledge wiki from someone's personal data. Not a filing clerk. A writer. Your job is to read entries, understand what they mean, and write articles that capture understanding. The wiki is a map of a mind.
A non-exhaustive list of WebGL and WebGPU frameworks and libraries. It is mostly for learning purposes as some of the libraries listed are wip/outdated/not maintained anymore.
| Name | Stars | Last Commit | Description |
|---|---|---|---|
| three.js | ![GitH |
| #!/usr/bin/env bash | |
| # Download VMware Workstation for Linux | |
| # Handles legacy versions (12.x - 17.6.3) from Cloudflare CDN cache and | |
| # newer versions (17.6.4+) from Archive.org. | |
| # | |
| # Use '-k' to keep the CDN download file compressed, exiting after download. (Ignored for v17.6.4+) | |
| # Use '-v <Version>' to specify desired version directly. | |
| BASE_URL="https://softwareupdate-prod.broadcom.com/cds/vmw-desktop/" |
| license: mit |
| name | measure-software-simplicity |
|---|---|
| description | Measure and report how well a design, pull request, module, service, refactor, or repository follows software-simplicity principles such as Single Responsibility, deep modules, single knowledge ownership, low change amplification, low caller burden, valid-state design, actionable failures, controlled scope, and evidence-based performance work. Use for architecture assessments, code reviews, maintainability audits, refactoring baselines, before-and-after comparisons, and engineering quality reports. |
Assess simplicity with Goal-Question-Metric (GQM), repository evidence, and a calibrated scorecard. Treat measurements as decision support.
Audit this entire codebase for materially useful simplifications in its data structures, state representation, control flow, algorithms, and ownership.
This is an audit-only exercise. Do not edit files, run tests, implement recommendations, commit, or push. Read-only inspection commands are allowed.
You are the coordinator. Continue until the complete codebase has been reviewed and the final audit is validated.
Inspect the repository and inventory every identifiable subsystem.
| name | review-with-me |
|---|---|
| description | Review a PR together — runs /code-review in the background with findings held back, quizzes you on the mechanism, callsites, and edge cases while you read the diff yourself, then reveals the findings, reconciles them against your answers, and posts the ones that survive as inline PR comments you approve one at a time. Use for "review this PR with me", "quiz me on PR N", "/review-with-me 371". |
A PR review is finished when the human understands the change, not when a tool prints findings. This skill runs the automated review and the human's own read in parallel, tests the human's understanding before showing any findings, and only then writes comments.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.